A Novel Framework of Software Reliability Evaluation with Software Reliability Growth Models and Software Metrics

This paper proposes a novel framework of software reliability growth models with software metrics. Our approach is to integrate a classical Poisson-regression-based fault prediction with non-homogeneous Poisson process based software reliability growth models. The remarkable feature of this approach is to handle time series data of fault detections and software metrics for a number of modules at the same time. In the paper, we present the modeling framework that combines Poisson-regression-based fault prediction and software reliability growth models, and also develop an efficient algorithm to estimate model parameters based on EM (expectation-maximization) algorithm. In numerical experiments, by comparing the proposed model with both Poisson-regression-based fault prediction and non-homogeneous Poisson process based software reliability growth models, we discuss the effectiveness of using time series data of fault detections and software metrics from both viewpoints of reliability estimation and fault prediction.